Deterministic and stochastic model predictive energy management of hybrid electric vehicles using two improved speed predictors

Abstract

The performance of model predictive control strategies for hybrid electric vehicles (HEVs) highly depends on the accuracy of future speed predictions. This paper proposes improved prediction models for deterministic model predictive control (DMPC) and stochastic model predictive control (SMPC), respectively. For DMPC, the neural network-based predictor is first introduced and taken as the benchmark predictor. A novel deterministic predictor considering historical prediction errors is proposed, which relies on the assumption that the offset between the prediction and measurement at current instant is a good estimate of the offset in the short future. Based on the proposed deterministic predictor, a stochastic predictor that considers the distribution law of historical data at different locations is further proposed for SMPC. Simulation results show that the controller using the proposed deterministic prediction model improves fuel economy by 2.89%, and the controller using the proposed stochastic prediction model improves fuel economy by 4.5% compared with the benchmark.